In this part, we are going to visualise the secretory cells after clustering.
It will include the figures we presented in our manuscript.
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Plot UMAP of all fresh cells
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## Ciliated Secretory
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Cluster 4 is characterized by upregulation of major histocompatibility complex (MHC) Class II genes (e.g. HLA-DQA1, HLA-DPA1 and HLA-DPB1), cytokeratins (KRT17, KRT5 and KRT23) and aldehyde dehydrogenases (e.g. ALDH1A1 and ALDH3B2)
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## R version 3.5.2 (2018-12-20)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Mojave 10.14.4
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
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## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
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## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 Seurat_2.3.4
## [3] Matrix_1.2-15 cowplot_0.9.4
## [5] DoubletFinder_2.0.1 reshape2_1.4.3
## [7] scales_1.0.0 dplyr_0.7.8
## [9] edgeR_3.24.3 limma_3.38.3
## [11] scater_1.10.1 ggplot2_3.2.0.9000
## [13] SingleCellExperiment_1.4.1 SummarizedExperiment_1.12.0
## [15] DelayedArray_0.8.0 BiocParallel_1.16.5
## [17] matrixStats_0.54.0 Biobase_2.42.0
## [19] GenomicRanges_1.34.0 GenomeInfoDb_1.18.1
## [21] IRanges_2.16.0 S4Vectors_0.20.1
## [23] BiocGenerics_0.28.0
##
## loaded via a namespace (and not attached):
## [1] snow_0.4-3 backports_1.1.3
## [3] Hmisc_4.2-0 plyr_1.8.4
## [5] igraph_1.2.3 lazyeval_0.2.1
## [7] splines_3.5.2 digest_0.6.18
## [9] foreach_1.4.4 htmltools_0.3.6
## [11] viridis_0.5.1 lars_1.2
## [13] gdata_2.18.0 magrittr_1.5
## [15] checkmate_1.9.1 cluster_2.0.7-1
## [17] mixtools_1.1.0 ROCR_1.0-7
## [19] R.utils_2.7.0 colorspace_1.4-0
## [21] xfun_0.4 jsonlite_1.6
## [23] crayon_1.3.4 RCurl_1.95-4.11
## [25] bindr_0.1.1 zoo_1.8-4
## [27] survival_2.43-3 iterators_1.0.10
## [29] ape_5.2 glue_1.3.0
## [31] gtable_0.2.0 zlibbioc_1.28.0
## [33] XVector_0.22.0 kernlab_0.9-27
## [35] Rhdf5lib_1.4.2 prabclus_2.2-7
## [37] DEoptimR_1.0-8 HDF5Array_1.10.1
## [39] mvtnorm_1.0-8 bibtex_0.4.2
## [41] Rcpp_1.0.0 metap_1.1
## [43] dtw_1.20-1 viridisLite_0.3.0
## [45] htmlTable_1.13.1 reticulate_1.10
## [47] foreign_0.8-71 bit_1.1-14
## [49] proxy_0.4-22 mclust_5.4.2
## [51] SDMTools_1.1-221 Formula_1.2-3
## [53] umap_0.1.0.3 tsne_0.1-3
## [55] htmlwidgets_1.3 httr_1.4.0
## [57] gplots_3.0.1.1 RColorBrewer_1.1-2
## [59] fpc_2.1-11.1 acepack_1.4.1
## [61] modeltools_0.2-22 ica_1.0-2
## [63] pkgconfig_2.0.2 R.methodsS3_1.7.1
## [65] flexmix_2.3-14 nnet_7.3-12
## [67] dynamicTreeCut_1.63-1 locfit_1.5-9.1
## [69] labeling_0.3 tidyselect_0.2.5
## [71] rlang_0.4.0 munsell_0.5.0
## [73] tools_3.5.2 ggridges_0.5.1
## [75] evaluate_0.13 stringr_1.4.0
## [77] yaml_2.2.0 npsurv_0.4-0
## [79] knitr_1.21 bit64_0.9-7
## [81] fitdistrplus_1.0-14 robustbase_0.93-3
## [83] caTools_1.17.1.1 purrr_0.3.0
## [85] RANN_2.6.1 pbapply_1.4-0
## [87] nlme_3.1-137 scran_1.10.2
## [89] R.oo_1.22.0 hdf5r_1.0.1
## [91] compiler_3.5.2 rstudioapi_0.9.0
## [93] png_0.1-7 beeswarm_0.2.3
## [95] lsei_1.2-0 statmod_1.4.30
## [97] tibble_2.0.1 stringi_1.2.4
## [99] lattice_0.20-38 trimcluster_0.1-2.1
## [101] pillar_1.3.1 lmtest_0.9-36
## [103] Rdpack_0.10-1 BiocNeighbors_1.0.0
## [105] data.table_1.12.0 bitops_1.0-6
## [107] irlba_2.3.3 gbRd_0.4-11
## [109] R6_2.3.0 latticeExtra_0.6-28
## [111] KernSmooth_2.23-15 gridExtra_2.3
## [113] vipor_0.4.5 codetools_0.2-16
## [115] MASS_7.3-51.1 gtools_3.8.1
## [117] assertthat_0.2.0 rhdf5_2.26.2
## [119] withr_2.1.2 GenomeInfoDbData_1.2.0
## [121] diptest_0.75-7 doSNOW_1.0.16
## [123] grid_3.5.2 rpart_4.1-13
## [125] tidyr_0.8.2 class_7.3-15
## [127] rmarkdown_1.11 DelayedMatrixStats_1.4.0
## [129] segmented_0.5-3.0 Rtsne_0.15
## [131] base64enc_0.1-3 ggbeeswarm_0.6.0